Purpose:
Runs survival analysis models using splicing cluster assignment and 1) single exon splicing burden index (SBI) or 2) KEGG Spliceosome GSVA scores as a predictor
Uses a wrapper function (survival_analysis) from utils
folder.
Load packages, set directory paths and call setup script
library(tidyverse)
library(survival)
library(ggpubr)
library(ggplot2)
library(patchwork)
root_dir <- rprojroot::find_root(rprojroot::has_dir(\.git\))
data_dir <- file.path(root_dir, \data\)
analysis_dir <- file.path(root_dir, \analyses\, \survival\)
input_dir <- file.path(analysis_dir, \results\)
results_dir <- file.path(analysis_dir, \results\)
plot_dir <- file.path(analysis_dir, \plots\)
# If the input and results directories do not exist, create it
if (!dir.exists(results_dir)) {
dir.create(results_dir, recursive = TRUE)
}
source(file.path(analysis_dir, \util\, \survival_models.R\))
Set metadata and cluster assignment file paths
metadata_file <- file.path(input_dir, \splicing_indices_with_survival.tsv\)
cluster_file <- file.path(root_dir, \analyses\,
\sample-psi-clustering\, \results\,
\sample-cluster-metadata-top-5000-events-stranded.tsv\)
kegg_scores_stranded_file <- file.path(root_dir, \analyses\,
\sample-psi-clustering\, \results\,
\gsva_output_stranded.tsv\)
Wrangle data Add cluster assignment and spliceosome gsva scores to
metadata and define column lgg_group (LGG or
non_LGG)
metadata <- read_tsv(metadata_file)
clusters <- read_tsv(cluster_file) %>%
dplyr::rename(Kids_First_Biospecimen_ID = sample_id)
gsva_scores <- read_tsv(kegg_scores_stranded_file) %>%
dplyr::filter(geneset == \KEGG_SPLICEOSOME\) %>%
dplyr::rename(spliceosome_gsva_score = score)
# how many clusters?
n_clust <- length(unique(clusters$cluster))
metadata <- metadata %>%
right_join(clusters %>% dplyr::select(Kids_First_Biospecimen_ID,
cluster)) %>%
left_join(gsva_scores %>% dplyr::select(sample_id,
spliceosome_gsva_score),
by = c(\Kids_First_Biospecimen_ID\ = \sample_id\)) %>%
dplyr::mutate(cluster = glue::glue(\Cluster {cluster}\)) %>%
dplyr::mutate(cluster = fct_relevel(cluster,
paste0(\Cluster \, 1:n_clust))) %>%
dplyr::mutate(lgg_group = case_when(
plot_group == \Low-grade glioma\ ~ \LGG\,
TRUE ~ \non-LGG\
)) %>%
dplyr::mutate(SBI = SI_Total * 10) %>%
dplyr::mutate(age_at_diagnosis_years = age_at_diagnosis_days/365.25)
Generate coxph models including extent of tumor resection, lgg group, and cluster assignment and SBI as covariates
add_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c(\Not Reported\, \Unavailable\),],
terms = \extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+SBI\,
file.path(results_dir, \cox_OS_additive_terms_resection_lgg_group_cluster_SBI.RDS\),
\multivariate\,
years_col = \OS_years\,
status_col = \OS_status\)
forest_os <- plotForest(readRDS(file.path(results_dir, \cox_OS_additive_terms_resection_lgg_group_cluster_SBI.RDS\)))
forest_os
ggsave(file.path(plot_dir, \forest_add_OS_resection_lgg_group_cluster_assignment_SBI.pdf\),
forest_os,
width = 10, height = 6, units = \in\,
device = \pdf\)
add_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c(\Not Reported\, \Unavailable\),],
terms = \extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+SBI\,
file.path(results_dir, \cox_EFS_additive_terms_resection_lgg_group_cluster_SBI.RDS\),
\multivariate\,
years_col = \EFS_years\,
status_col = \EFS_status\)
forest_efs <- plotForest(readRDS(file.path(results_dir, \cox_EFS_additive_terms_resection_lgg_group_cluster_SBI.RDS\)))
forest_efs
ggsave(file.path(plot_dir, \forest_add_EFS_resection_lgg_group_cluster_assignment_SBI.pdf\),
forest_efs,
width = 10, height = 6, units = \in\,
device = \pdf\)
repeat analysis, replacing SBI with KEGG spliceosome gsva score
add_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c(\Not Reported\, \Unavailable\),],
terms = \extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+spliceosome_gsva_score\,
file.path(results_dir, \cox_OS_additive_terms_resection_lgg_group_cluster_spliceosome_score.RDS\),
\multivariate\,
years_col = \OS_years\,
status_col = \OS_status\)
forest_os <- plotForest(readRDS(file.path(results_dir, \cox_OS_additive_terms_resection_lgg_group_cluster_spliceosome_score.RDS\)))
forest_os
ggsave(file.path(plot_dir, \forest_add_OS_resection_lgg_group_cluster_assignment_spliceosome_score.pdf\),
forest_os,
width = 10, height = 6, units = \in\,
device = \pdf\)
models <- c(\spliceosome_gsva_score\, \SBI\)
for (each in models) {
int_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c(\Not Reported\, \Unavailable\),],
terms = paste0(\extent_of_tumor_resection+lgg_group+cluster*\, each, \+age_at_diagnosis_years\),
file.path(results_dir, paste0(\cox_EFS_interaction_terms_resection_lgg_group_cluster_\, each, \.RDS\)),
\multivariate\,
years_col = \EFS_years\,
status_col = \EFS_status\)
int_forest_efs <- plotForest(readRDS(file.path(results_dir, paste0(\cox_EFS_interaction_terms_resection_lgg_group_cluster_\, each, \.RDS\))))
int_forest_efs
ggsave(file.path(plot_dir, paste0(\forest_int_EFS_resection_lgg_group_cluster_assignment_\, each, \.pdf\)),
int_forest_efs,
width = 10, height = 6, units = \in\,
device = \pdf\)
int_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c(\Not Reported\, \Unavailable\),],
terms = paste0(\extent_of_tumor_resection+lgg_group+cluster*\, each, \+age_at_diagnosis_years\),
file.path(results_dir, paste0(\cox_OS_interaction_terms_resection_lgg_group_cluster_\, each, \.RDS\)),
\multivariate\,
years_col = \OS_years\,
status_col = \OS_status\)
int_forest_os <- plotForest(readRDS(file.path(results_dir, paste0(\cox_OS_interaction_terms_resection_lgg_group_cluster_\, each, \.RDS\))))
int_forest_os
ggsave(file.path(plot_dir, paste0(\forest_int_OS_resection_lgg_group_cluster_assignment_\, each, \.pdf\)),
int_forest_os,
width = 10, height = 6, units = \in\,
device = \pdf\)
}
add_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c(\Not Reported\, \Unavailable\),],
terms = \extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+spliceosome_gsva_score\,
file.path(results_dir, \cox_EFS_additive_terms_resection_lgg_group_cluster_spliceosome_score.RDS\),
\multivariate\,
years_col = \EFS_years\,
status_col = \EFS_status\)
forest_efs <- plotForest(readRDS(file.path(results_dir, \cox_EFS_additive_terms_resection_lgg_group_cluster_spliceosome_score.RDS\)))
forest_efs
ggsave(file.path(plot_dir, \forest_add_EFS_resection_lgg_group_cluster_assignment_spliceosome_score.pdf\),
forest_efs,
width = 10, height = 6, units = \in\,
device = \pdf\)
Subset metadata for LGG, and only include clusters with
>= 10 samples
lgg <- metadata %>%
dplyr::filter(plot_group == \Low-grade glioma\) %>%
dplyr::mutate(cluster = factor(cluster)) %>%
dplyr::mutate(mol_sub_group = fct_relevel(mol_sub_group, \Wildtype\, after = 0))
retain_clusters_lgg <- lgg %>%
dplyr::count(cluster) %>%
filter(n >= 10) %>%
pull(cluster)
lgg <- lgg %>%
filter(cluster %in% retain_clusters_lgg) %>%
dplyr::mutate(cluster = factor(cluster))
Generate coxph models including covariates
extent_of_tumor_resection, mol_sub_group,
cluster, and SBI and plot
# identify LGG clusters
lgg_clusters <- metadata %>%
filter(lgg_group == \LGG\) %>%
mutate(cluster = as.integer(gsub(\cluster\, \\, cluster))) %>%
pull(cluster) %>%
sort() %>%
unique()
add_model_lgg_efs <- fit_save_model(lgg[!lgg$cluster %in% lgg_clusters & !lgg$extent_of_tumor_resection %in% c(\Not Reported\, \Unavailable\),],
terms = \extent_of_tumor_resection+mol_sub_group+cluster+age_at_diagnosis_years+SBI\,
file.path(results_dir, \cox_lgg_EFS_additive_terms_resection_subtype_cluster_SBI.RDS\),
\multivariate\,
years_col = \EFS_years\,
status_col = \EFS_status\)
forest_lgg_efs <- plotForest(readRDS(file.path(results_dir, \cox_lgg_EFS_additive_terms_resection_subtype_cluster_SBI.RDS\)))
forest_lgg_efs
ggsave(file.path(plot_dir, \forest_add_EFS_LGG_resection_subtype_cluster_assignment_SBI.pdf\),
forest_lgg_efs,
width = 10, height = 6, units = \in\,
device = \pdf\)
repeat analysis replacing SBI with
spliceosome_gsva_score
add_model_lgg_efs <- fit_save_model(lgg[!lgg$cluster %in% lgg_clusters & !lgg$extent_of_tumor_resection %in% c(\Not Reported\, \Unavailable\),],
terms = \extent_of_tumor_resection+mol_sub_group+cluster+age_at_diagnosis_years+spliceosome_gsva_score\,
file.path(results_dir, \cox_lgg_EFS_additive_terms_resection_subtype_cluster_spliceosome_score.RDS\),
\multivariate\,
years_col = \EFS_years\,
status_col = \EFS_status\)
forest_lgg_efs <- plotForest(readRDS(file.path(results_dir, \cox_lgg_EFS_additive_terms_resection_subtype_cluster_spliceosome_score.RDS\)))
forest_lgg_efs
ggsave(file.path(plot_dir, \forest_add_EFS_LGG_resection_subtype_cluster_assignment_spliceosome_score.pdf\),
forest_lgg_efs,
width = 10, height = 6, units = \in\,
device = \pdf\)
Subset metadata for HGG and retain cluster with n >=
10
hgg <- metadata %>%
dplyr::filter(plot_group %in% c(\Other high-grade glioma\, \Diffuse midline glioma\)) %>%
dplyr::mutate(cluster = factor(cluster)) %>%
dplyr::mutate(mol_sub_group = fct_relevel(mol_sub_group, \HGG
Generate HGG KM models with spliceosome_group as
covariate
# Generate kaplan meier survival models for OS and EFS, and save outputs
hgg_kap_os <- survival_analysis(
metadata = hgg %>% dplyr::filter(!is.na(spliceosome_group)),
ind_var = \spliceosome_group\,
test = \kap.meier\,
metadata_sample_col = \Kids_First_Biospecimen_ID\,
days_col = \OS_days\,
status_col = \OS_status\
)
readr::write_rds(hgg_kap_os,
file.path(results_dir, \logrank_hgg_OS_splice_group.RDS\))
hgg_kap_efs <- survival_analysis(
metadata = hgg %>% dplyr::filter(!is.na(spliceosome_group)),
ind_var = \spliceosome_group\,
test = \kap.meier\,
metadata_sample_col = \Kids_First_Biospecimen_ID\,
days_col = \EFS_days\,
status_col = \EFS_status\
)
readr::write_rds(hgg_kap_efs,
file.path(results_dir, \logrank_hgg_EFS_splice_group.RDS\))
Generate KM plots
km_hgg_os_plot <- plotKM(model = hgg_kap_os,
variable = \spliceosome_group\,
combined = F,
title = \HGG
ggsave(file.path(plot_dir, \km_hgg_OS_spliceosome_score.pdf\),
km_hgg_os_plot,
width = 9, height = 5, units = \in\,
device = \pdf\)
km_hgg_efs_plot <- plotKM(model = hgg_kap_efs,
variable = \spliceosome_group\,
combined = F,
title = \HGG
ggsave(file.path(plot_dir, \km_hgg_EFS_spliceosome_score.pdf\),
km_hgg_efs_plot,
width = 9, height = 5, units = \in\,
device = \pdf\)
Generate coxph models for HGG including covariates
mol_sub_group cluster, and SBI,
and plot
add_model_hgg_os <- fit_save_model(hgg,
terms = \mol_sub_group+age_at_diagnosis_years+SBI\,
file.path(results_dir, \cox_hgg_OS_additive_terms_subtype_cluster_SBI.RDS\),
\multivariate\,
years_col = \OS_years\,
status_col = \OS_status\)
forest_hgg_os <- plotForest(readRDS(file.path(results_dir, \cox_hgg_OS_additive_terms_subtype_cluster_SBI.RDS\)))
forest_hgg_os
ggsave(file.path(plot_dir, \forest_add_OS_HGG_subtype_cluster_assignment_SBI.pdf\),
forest_hgg_os,
width = 9, height = 5, units = \in\,
device = \pdf\)
add_model_hgg_efs <- fit_save_model(hgg,
terms = \mol_sub_group+age_at_diagnosis_years+SBI\,
file.path(results_dir, \cox_hgg_EFS_additive_terms_subtype_cluster_SBI.RDS\),
\multivariate\,
years_col = \EFS_years\,
status_col = \EFS_status\)
forest_hgg_efs <- plotForest(readRDS(file.path(results_dir, \cox_hgg_EFS_additive_terms_subtype_cluster_SBI.RDS\)))
ggsave(file.path(plot_dir, \forest_add_EFS_HGG_subtype_cluster_assignment_SBI.pdf\),
forest_hgg_efs,
width = 9, height = 5, units = \in\,
device = \pdf\)
Repeat analysis replacing SBI with
spliceosome_gsva_score
add_model_hgg_os <- fit_save_model(hgg,
terms = \mol_sub_group+age_at_diagnosis_years+spliceosome_gsva_score\,
file.path(results_dir, \cox_hgg_OS_additive_terms_subtype_cluster_spliceosome_score.RDS\),
\multivariate\,
years_col = \OS_years\,
status_col = \OS_status\)
forest_hgg_os <- plotForest(readRDS(file.path(results_dir, \cox_hgg_OS_additive_terms_subtype_cluster_spliceosome_score.RDS\)))
forest_hgg_os
ggsave(file.path(plot_dir, \forest_add_OS_HGG_subtype_cluster_assignment_spliceosome_score.pdf\),
forest_hgg_os,
width = 9, height = 5, units = \in\,
device = \pdf\)
add_model_hgg_efs <- fit_save_model(hgg,
terms = \mol_sub_group+age_at_diagnosis_years+spliceosome_gsva_score\,
file.path(results_dir, \cox_hgg_EFS_additive_terms_subtype_cluster_spliceosome_score.RDS\),
\multivariate\,
years_col = \EFS_years\,
status_col = \EFS_status\)
forest_hgg_efs <- plotForest(readRDS(file.path(results_dir, \cox_hgg_EFS_additive_terms_subtype_cluster_spliceosome_score.RDS\)))
ggsave(file.path(plot_dir, \forest_add_EFS_HGG_subtype_cluster_assignment_spliceosome_score.pdf\),
forest_hgg_efs,
width = 9, height = 5, units = \in\,
device = \pdf\)
Filter for cluster 7
cluster7_df <- metadata %>%
dplyr::filter(cluster == \Cluster 7\,
!is.na(EFS_days)) %>%
dplyr::mutate(SI_group = case_when(
SBI > summary(SBI)[\3rd Qu.\] ~ \High SBI\,
SBI < summary(SBI)[\1st Qu.\] ~ \Low SBI\,
TRUE ~ NA_character_
)) %>%
dplyr::mutate(spliceosome_group = case_when(
spliceosome_gsva_score > summary(spliceosome_gsva_score)[\3rd Qu.\] ~ \Splice GSVA 4th Q\,
spliceosome_gsva_score > summary(spliceosome_gsva_score)[\Median\] ~ \Splice GSVA 3rd Q\,
spliceosome_gsva_score > summary(spliceosome_gsva_score)[\1st Qu.\] ~ \Splice GSVA 2nd Q\,
TRUE ~ \Splice GSVA 1st Q\
)) %>%
dplyr::mutate(SI_group = fct_relevel(SI_group,
c(\High SBI\, \Low SBI\))) %>%
dplyr::mutate(spliceosome_group = fct_relevel(spliceosome_group,
c(\Splice GSVA 1st Q\,
\Splice GSVA 2nd Q\,
\Splice GSVA 3rd Q\,
\Splice GSVA 4th Q\)))
Generate KM models with SI_group as covariate
# Generate kaplan meier survival models for OS and EFS, and save outputs
c7_si_kap_os <- survival_analysis(
metadata = cluster7_df %>% dplyr::filter(!is.na(SI_group)),
ind_var = \SI_group\,
test = \kap.meier\,
metadata_sample_col = \Kids_First_Biospecimen_ID\,
days_col = \OS_days\,
status_col = \OS_status\
)
readr::write_rds(c7_si_kap_os,
file.path(results_dir, \logrank_cluster7_OS_SBI.RDS\))
c7_si_kap_efs <- survival_analysis(
metadata = cluster7_df %>% dplyr::filter(!is.na(SI_group)),
ind_var = \SI_group\,
test = \kap.meier\,
metadata_sample_col = \Kids_First_Biospecimen_ID\,
days_col = \EFS_days\,
status_col = \EFS_status\
)
readr::write_rds(c7_si_kap_efs,
file.path(results_dir, \logrank_cluster7_EFS_SBI.RDS\))
Generate Cluster 7 KM SI_group plots
km_c7_si_os_plot <- plotKM(model = c7_si_kap_os,
variable = \SI_group\,
combined = F,
title = \Cluster 7
ggsave(file.path(plot_dir, \km_cluster7_OS_sbi_group.pdf\),
km_c7_si_os_plot,
width = 8, height = 5, units = \in\,
device = \pdf\)
km_c7_si_efs_plot <- plotKM(model = c7_si_kap_efs,
variable = \SI_group\,
combined = F,
title = \Cluster 7
ggsave(file.path(plot_dir, \km_cluster7_EFS_sbi_group.pdf\),
km_c7_si_efs_plot,
width = 8, height = 5, units = \in\,
device = \pdf\)
Generate KM models with spliceosome_group as
covariate
# Generate kaplan meier survival models for OS and EFS, and save outputs
c7_splice_kap_os <- survival_analysis(
metadata = cluster7_df %>%
dplyr::filter(spliceosome_group %in% c(\Splice GSVA 4th Q\, \Splice GSVA 1st Q\)) %>%
dplyr::mutate(spliceosome_group = factor(spliceosome_group,
levels = c(\Splice GSVA 1st Q\, \Splice GSVA 4th Q\))),
ind_var = \spliceosome_group\,
test = \kap.meier\,
metadata_sample_col = \Kids_First_Biospecimen_ID\,
days_col = \OS_days\,
status_col = \OS_status\
)
readr::write_rds(c7_splice_kap_os,
file.path(results_dir, \logrank_cluster7_OS_splice_group.RDS\))
c7_splice_kap_efs <- survival_analysis(
metadata = cluster7_df %>%
dplyr::filter(spliceosome_group %in% c(\Splice GSVA 4th Q\, \Splice GSVA 1st Q\)) %>%
dplyr::mutate(spliceosome_group = factor(spliceosome_group,
levels = c(\Splice GSVA 1st Q\, \Splice GSVA 4th Q\))),
ind_var = \spliceosome_group\,
test = \kap.meier\,
metadata_sample_col = \Kids_First_Biospecimen_ID\,
days_col = \EFS_days\,
status_col = \EFS_status\
)
readr::write_rds(c7_splice_kap_efs,
file.path(results_dir, \logrank_cluster7_EFS_splice_group.RDS\))
Generate Cluster 7 KM spliceosome_group plots
km_c7_splice_os_plot <- plotKM(model = c7_splice_kap_os,
variable = \spliceosome_group\,
combined = F,
title = \Cluster 7
ggsave(file.path(plot_dir, \km_cluster7_OS_splice_group.pdf\),
km_c7_splice_os_plot,
width = 9, height = 5, units = \in\,
device = \pdf\)
km_c7_splice_efs_plot <- plotKM(model = c7_splice_kap_efs,
variable = \spliceosome_group\,
combined = F,
title = \Cluster 7
ggsave(file.path(plot_dir, \km_cluster7_EFS_splice_group.pdf\),
km_c7_splice_efs_plot,
width = 9, height = 5, units = \in\,
device = \pdf\)
Assess EFS and OS by SBI or spliceosome GSVA score in multivariate models and generate forest plots
add_model_c7_efs <- fit_save_model(cluster7_df %>%
dplyr::filter(extent_of_tumor_resection != \Unavailable\,
spliceosome_group %in% c(\Splice GSVA 4th Q\, \Splice GSVA 1st Q\)) %>%
dplyr::mutate(plot_group = fct_relevel(plot_group, \Low-grade glioma\, after = 0)),
terms = \extent_of_tumor_resection+age_at_diagnosis_years+plot_group+spliceosome_group\,
file.path(results_dir, \cox_hgg_EFS_additive_terms_subtype_cluster_spliceosome_score.RDS\),
\multivariate\,
years_col = \EFS_years\,
status_col = \EFS_status\)
forest_c7_spliceosome_efs <- plotForest(readRDS(file.path(results_dir, \cox_hgg_EFS_additive_terms_subtype_cluster_spliceosome_score.RDS\)))
ggsave(file.path(plot_dir, \forest_add_EFS_cluster7_histology_resection_spliceosome_group.pdf\),
forest_c7_spliceosome_efs,
width = 9, height = 4, units = \in\,
device = \pdf\)
add_model_c7_os <- fit_save_model(cluster7_df %>%
dplyr::filter(!extent_of_tumor_resection %in% c(\Not Reported\, \Unavailable\)) %>%
dplyr::mutate(plot_group = fct_relevel(plot_group, \Low-grade glioma\, after = 0)),
terms = \extent_of_tumor_resection+age_at_diagnosis_years+plot_group+SBI\,
file.path(results_dir, \cox_hgg_OS_additive_terms_subtype_cluster_si_group.RDS\),
\multivariate\,
years_col = \OS_years\,
status_col = \OS_status\)
forest_c7_si_os <- plotForest(readRDS(file.path(results_dir, \cox_hgg_OS_additive_terms_subtype_cluster_si_group.RDS\)))
ggsave(file.path(plot_dir, \forest_add_OS_cluster7_histology_resection_si.pdf\),
forest_c7_si_os,
width = 9, height = 4, units = \in\,
device = \pdf\)
Print session info
sessionInfo()
R version 4.4.0 (2024-04-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.4 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] gtools_3.9.5 survminer_0.4.9 patchwork_1.2.0 ggpubr_0.6.0
[5] survival_3.7-0 lubridate_1.9.4 forcats_1.0.1 stringr_1.6.0
[9] dplyr_1.1.4 purrr_1.2.0 readr_2.1.6 tidyr_1.3.1
[13] tibble_3.3.0 ggplot2_4.0.1 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] gtable_0.3.6 xfun_0.54 bslib_0.9.0 rstatix_0.7.2
[5] lattice_0.22-7 tzdb_0.5.0 vctrs_0.6.5 tools_4.4.0
[9] generics_0.1.4 parallel_4.4.0 pkgconfig_2.0.3 Matrix_1.7-4
[13] data.table_1.17.8 RColorBrewer_1.1-3 S7_0.2.1 lifecycle_1.0.4
[17] compiler_4.4.0 farver_2.1.2 textshaping_1.0.4 carData_3.0-5
[21] colorblindr_0.1.0 htmltools_0.5.8.1 sass_0.4.10 yaml_2.3.10
[25] crayon_1.5.3 pillar_1.11.1 car_3.1-2 jquerylib_0.1.4
[29] cachem_1.1.0 abind_1.4-5 km.ci_0.5-6 commonmark_2.0.0
[33] tidyselect_1.2.1 digest_0.6.39 stringi_1.8.7 labeling_0.4.3
[37] splines_4.4.0 cowplot_1.1.3 rprojroot_2.1.1 fastmap_1.2.0
[41] grid_4.4.0 colorspace_2.1-2 cli_3.6.5 magrittr_2.0.4
[45] broom_1.0.10 withr_3.0.2 scales_1.4.0 backports_1.5.0
[49] bit64_4.6.0-1 timechange_0.3.0 rmarkdown_2.30 ggtext_0.1.2
[53] bit_4.6.0 gridExtra_2.3 ggsignif_0.6.4 ragg_1.5.0
[57] zoo_1.8-12 hms_1.1.4 evaluate_1.0.5 knitr_1.50
[61] KMsurv_0.1-5 markdown_1.13 survMisc_0.5.6 rlang_1.1.6
[65] Rcpp_1.1.0 gridtext_0.1.5 xtable_1.8-4 glue_1.8.0
[69] xml2_1.5.0 vroom_1.6.6 jsonlite_2.0.0 R6_2.6.1
[73] systemfonts_1.3.1